Reconciliation of regional travel model and passive device tracking data 14 th TRB Planning Applications Conference Leta F. Huntsinger Rick Donnelly.

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Presentation transcript:

Reconciliation of regional travel model and passive device tracking data 14 th TRB Planning Applications Conference Leta F. Huntsinger Rick Donnelly

Introduction 2  Passively collected mobile phone data has shown promise as a low cost option for obtaining travel data:  Speed data (Using Cell Phone Technology to Collect Travel Data, Kyle Ward)  Trip tables (Origin Destination Study using Cellular Technology for Mobile, Al, Kevin Harrison)  Freight Data (Freight Data Collection Technique and Algorithm using Cellular Phone and GIS Data, Ming-Heng Wang, et. al.)  other  Comparison of passively collected data against traditionally collected survey data

Challenges 3  Household surveys  behaviorally rich, but small sample size at TAZ to TAZ level  Small TAZ to TAZ observations limit our understanding of flows at the sub-district level  Many small MPOs cannot afford household surveys  Trip distribution parameters are the most challenging to transfer  Passively collected data  Large sample size, but lacks behavioral richness

Data – Air Sage 4

Triangle Regional Model 5

Process 6 Disagg to TAZ Apply factors to AirSage matrix Add IE, EI, and EE trips to AirSage matrix Develop AM factors from TRM data Apply AM factors to AirSage matrix Convert AirSage person trips to vehicle trips AM peak hour assignment of AirSage AM peak hour assignment of TRM Summarize MOEs and compare

Results – travel time comparisons 7 Average Trip Length (TT) TRM14.42 Air Sage15.51 TRM – slightly higher % of shorter trips

Results – district to district flows 8 District Map District Trip Table Color Coded by Absolute and Relative Error

Results – Assignment MOEs 9 Functional Classification 23 – 26 are rural facilities

Results – Assignment MOEs 10

Results – Assignment MOEs 11

Results – Assignment MOEs 12

Findings and Recommendations 13  Early data set – includes Sprint data only  Great source of validation data  Low cost option  Lacks behavioral richness of household survey  Larger sample than household survey  Continuing improvements are needed  Useful to validate an estimated trip table  Add to toolbox

Acknowledgements 14  Co-author – Rick Donnelly  Kyle Ward, CAMPO  Air Sage

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